Dynamo: Amazon’s Highly Available Key-value Store

Dynamo: Amazon’s Highly Available Key-value Store is reprinted here in its
entirety, images and all.

Throughout the paper you will find notes containing Riak KV-specifics that
relate to a given section of the paper; anything from links to the docs, to
code references, to explanations of why and how we did what we did.

This paper was first released in 2007 and was popularized on the blog of
Werner Vogels. Since then, several Dynamo-inspired databases have appeared
(either entirely or partially) by this paper. In addition to Riak KV,
Cassandra and Voldemort come to mind. You may also remember Dynomite (which
predates all of these). There are probably more.

Also note that this paper has little to do with Amazon’s DynamoDB service.
They have not published the inner workings of that implementation.

Abstract

Reliability at massive scale is one of the biggest challenges we face at
Amazon.com, one of the largest e-commerce operations in the world; even the
slightest outage has significant financial consequences and impacts customer
trust. The Amazon.com platform, which provides services for many web sites
worldwide, is implemented on top of an infrastructure of tens of thousands of
servers and network components located in many datacenters around the world. At
this scale, small and large components fail continuously and the way persistent
state is managed in the face of these failures drives the reliability and
scalability of the software systems.

This paper presents the design and implementation of Dynamo, a highly available
key-value storage system that some of Amazon’s core services use to provide an
“always-on” experience. To achieve this level of availability, Dynamo sacrifices
consistency under certain failure scenarios. It makes extensive use of object
versioning and application-assisted conflict resolution in a manner that
provides a novel interface for developers to use.

1. Introduction

Amazon runs a world-wide e-commerce platform that serves tens of millions
customers at peak times using tens of thousands of servers located in many data
centers around the world. There are strict operational requirements on Amazon’s
platform in terms of performance, reliability and efficiency, and to support
continuous growth the platform needs to be highly scalable. Reliability is one
of the most important requirements because even the slightest outage has
significant financial consequences and impacts customer trust. In addition, to
support continuous growth, the platform needs to be highly scalable.

One of the lessons our organization has learned from operating Amazon’s platform
is that the reliability and scalability of a system is dependent on how its
application state is managed. Amazon uses a highly decentralized, loosely
coupled, service oriented architecture consisting of hundreds of services. In
this environment there is a particular need for storage technologies that are
always available. For example, customers should be able to view and add items to
their shopping cart even if disks are failing, network routes are flapping, or
data centers are being destroyed by tornados. Therefore, the service responsible
for managing shopping carts requires that it can always write to and read from
its data store, and that its data needs to be available across multiple data
centers.

Dealing with failures in an infrastructure comprised of millions of components
is our standard mode of operation; there are always a small but significant
number of server and network components that are failing at any given time. As
such Amazon’s software systems need to be constructed in a manner that treats
failure handling as the normal case without impacting availability or
performance.

To meet the reliability and scaling needs, Amazon has developed a number of
storage technologies, of which the Amazon Simple Storage Service (also available
outside of Amazon and known as Amazon S3), is probably the best known. This
paper presents the design and implementation of Dynamo, another highly available
and scalable distributed data store built for Amazon’s platform. Dynamo is used
to manage the state of services that have very high reliability requirements and
need tight control over the tradeoffs between availability, consistency, cost-
effectiveness and performance. Amazon’s platform has a very diverse set of
applications with different storage requirements. A select set of applications
requires a storage technology that is flexible enough to let application
designers configure their data store appropriately based on these tradeoffs to
achieve high availability and guaranteed performance in the most cost effective
manner.

There are many services on Amazon’s platform that only need primary-key access
to a data store. For many services, such as those that provide best seller
lists, shopping carts, customer preferences, session management, sales rank, and
product catalog, the common pattern of using a relational database would lead to
inefficiencies and limit scale and availability. Dynamo provides a simple
primary-key only interface to meet the requirements of these applications.

Dynamo uses a synthesis of well known techniques to achieve scalability and
availability: Data is partitioned and replicated using consistent hashing [10],
and consistency is facilitated by object versioning [12]. The consistency among
replicas during updates is maintained by a quorum-like technique and a
decentralized replica synchronization protocol. Dynamo employs a gossip based
distributed failure detection and membership protocol. Dynamo is a completely
decentralized system with minimal need for manual administration. Storage nodes
can be added and removed from Dynamo without requiring any manual partitioning
or redistribution.

Like Dynamo, Riak KV employs consistent hashing to partition and replicate
data around the ring. For the consistent hashing that takes place in
riak_core, Basho chose the SHA1 hash. See Consistent Hashing in our docs.

Riak KV uses vector clocks for object versioning. Scroll down to section 4.4
to read up on this in depth.

Riak KV makes use of gossiping in the same way that Dynamo does: to
communicate ring state and node membership. See Gossip Protocol in our docs.

And, nodes can be added and removed from your Riak cluster as needed.

In the past year, Dynamo has been the underlying storage technology for a number
of the core services in Amazon’s e-commerce platform. It was able to scale to
extreme peak loads efficiently without any downtime during the busy holiday
shopping season. For example, the service that maintains shopping cart (Shopping
Cart Service) served tens of millions requests that resulted in well over 3
million checkouts in a single day and the service that manages session state
handled hundreds of thousands of concurrently active sessions.

The main contribution of this work for the research community is the evaluation
of how different techniques can be combined to provide a single highly-available
system. It demonstrates that an eventually-consistent storage system can be used
in production with demanding applications. It also provides insight into the
tuning of these techniques to meet the requirements of production systems with
very strict performance demands.

The paper is structured as follows. Section 2 presents the background and
Section 3 presents the related work. Section 4 presents the system design and
Section 5 describes the implementation. Section 6 details the experiences and
insights gained by running Dynamo in production and Section 7 concludes the
paper. There are a number of places in this paper where additional information
may have been appropriate but where protecting Amazon’s business interests
require us to reduce some level of detail. For this reason, the intra- and
inter-datacenter latencies in section 6, the absolute request rates in section
6.2 and outage lengths and workloads in section 6.3 are provided through
aggregate measures instead of absolute details.

2. Background

Amazon’s e-commerce platform is composed of hundreds of services that work in
concert to deliver functionality ranging from recommendations to order
fulfillment to fraud detection. Each service is exposed through a well defined
interface and is accessible over the network. These services are hosted in an
infrastructure that consists of tens of thousands of servers located across many
data centers world-wide. Some of these services are stateless (i.e., services
which aggregate responses from other services) and some are stateful (i.e., a
service that generates its response by executing business logic on its state
stored in persistent store).

Brief Background on Riak KV

Basho Technologies started to develop Riak KV back in 2007 to solve an
internal problem. We were, at the time, builing a web application that would
require a database layer that afforded higher availability and scale out
properties than any technology we knew of. So, we rolled our own.

After using Riak KV in production for several successful applications that
generated revenue, we decided to open source it and share our creation with
the world.

Traditionally production systems store their state in relational databases. For
many of the more common usage patterns of state persistence, however, a
relational database is a solution that is far from ideal. Most of these services
only store and retrieve data by primary key and do not require the complex
querying and management functionality offered by an RDBMS. This excess
functionality requires expensive hardware and highly skilled personnel for its
operation, making it a very inefficient solution. In addition, the available
replication technologies are limited and typically choose consistency over
availability. Although many advances have been made in the recent years, it is
still not easy to scale-out databases or use smart partitioning schemes for load
balancing.

This paper describes Dynamo, a highly available data storage technology that
addresses the needs of these important classes of services. Dynamo has a simple
key/value interface, is highly available with a clearly defined consistency
window, is efficient in its resource usage, and has a simple scale out scheme to
address growth in data set size or request rates. Each service that uses Dynamo
runs its own Dynamo instances.

Riak KV is a highly available, scalable, open source key/value database. These
notes describe where Riak KV’s design decisions emulated and diverged from
Dynamo’s (as described in this paper).

Riak KV offers several query methods in addition to the standard key/value
interface, is made to be highly-available, is efficient in its resource uses,
and has a simple scale out story to accompany data and traffic growth.

2.1 System Assumptions and Requirements

The storage system for this class of services has the following requirements:

Query Model: simple read and write operations to a data item that is uniquely
identified by a key. State is stored as binary objects (i.e., blobs) identified
by unique keys. No operations span multiple data items and there is no need for
relational schema. This requirement is based on the observation that a
significant portion of Amazon’s services can work with this simple query model
and do not need any relational schema. Dynamo targets applications that need to
store objects that are relatively small (usually less than 1 MB).

ACID Properties: ACID (Atomicity, Consistency, Isolation, Durability) is a set
of properties that guarantee that database transactions are processed reliably.
In the context of databases, a single logical operation on the data is called a
transaction. Experience at Amazon has shown that data stores that provide ACID
guarantees tend to have poor availability. This has been widely acknowledged by
both the industry and academia [5]. Dynamo targets applications that operate
with weaker consistency (the “C” in ACID) if this results in high availability.
Dynamo does not provide any isolation guarantees and permits only single key
updates.

ACID?

Riak KV offers no traditional “ACID” semantics around transactions. Instead,
it’s built to be “eventually consistent.” We did this because we were of the
opinion (and our users proved this out)that most applications don’t require
heavy transactions. (Even ATMs are eventually consistent.)

Efficiency: The system needs to function on a commodity hardware
infrastructure. In Amazon’s platform, services have stringent latency
requirements which are in general measured at the 99.9th percentile of the
distribution. Given that state access plays a crucial role in service operation
the storage system must be capable of meeting such stringent SLAs (see Section
2.2 below). Services must be able to configure Dynamo such that they
consistently achieve their latency and throughput requirements. The tradeoffs
are in performance, cost efficiency, availability, and durability guarantees.

Other Assumptions: Dynamo is used only by Amazon’s internal services. Its
operation environment is assumed to be non-hostile and there are no security
related requirements such as authentication and authorization. Moreover, since
each service uses its distinct instance of Dynamo, its initial design targets a
scale of up to hundreds of storage hosts. We will discuss the scalability
limitations of Dynamo and possible scalability related extensions in later
sections.

2.2 Service Level Agreements (SLA)

To guarantee that the application can deliver its functionality in a bounded
time, each and every dependency in the platform needs to deliver its
functionality with even tighter bounds. Clients and services engage in a Service
Level Agreement (SLA), a formally negotiated contract where a client and a
service agree on several system-related characteristics, which most prominently
include the client’s expected request rate distribution for a particular API and
the expected service latency under those conditions. An example of a simple SLA
is a service guaranteeing that it will provide a response within 300ms for 99.9%
of its requests for a peak client load of 500 requests per second.

In Amazon’s decentralized service oriented infrastructure, SLAs play an
important role. For example a page request to one of the e-commerce sites
typically requires the rendering engine to construct its response by sending
requests to over 150 services. These services often have multiple dependencies,
which frequently are other services, and as such it is not uncommon for the call
graph of an application to have more than one level. To ensure that the page
rendering engine can maintain a clear bound on page delivery each service within
the call chain must obey its performance contract.

Riak KV Loves SLAs

Much like Amazon built Dynamo to guarantee their applications were always
available to retail shoppers, the design decisions in Riak KV were taken to
ensure that developers could sleep well knowing that their database would
always be available to serve requests.

Many of our clients and open source users have explicit uptime agreements
related to their applications and services built on Riak KV. This was not an
accident.

Figure 1 shows an abstract view of the architecture of
Amazon’s platform, where dynamic web content is generated by page rendering
components which in turn query many other services. A service can use different
data stores to manage its state and these data stores are only accessible within
its service boundaries. Some services act as aggregators by using several other
services to produce a composite response. Typically, the aggregator services are
stateless, although they use extensive caching.

Figure 1: Service-oriented architecture of Amazon’s platform.

A common approach in the industry for forming a performance oriented SLA is to
describe it using average, median and expected variance. At Amazon we have found
that these metrics are not good enough if the goal is to build a system where
all customers have a good experience, rather than just the majority. For example
if extensive personalization techniques are used then customers with longer
histories require more processing which impacts performance at the high-end of
the distribution. An SLA stated in terms of mean or median response times will
not address the performance of this important customer segment. To address this
issue, at Amazon, SLAs are expressed and measured at the 99.9th percentile of
the distribution. The choice for 99.9% over an even higher percentile has been
made based on a cost-benefit analysis which demonstrated a significant increase
in cost to improve performance that much. Experiences with Amazon’s production
systems have shown that this approach provides a better overall experience
compared to those systems that meet SLAs defined based on the mean or median.

In this paper there are many references to this 99.9th percentile of
distributions, which reflects Amazon engineers’ relentless focus on performance
from the perspective of the customers’ experience. Many papers report on
averages, so these are included where it makes sense for comparison purposes.
Nevertheless, Amazon’s engineering and optimization efforts are not focused on
averages. Several techniques, such as the load balanced selection of write
coordinators, are purely targeted at controlling performance at the 99.9th
percentile.

Storage systems often play an important role in establishing a service’s SLA,
especially if the business logic is relatively lightweight, as is the case for
many Amazon services. State management then becomes the main component of a
service’s SLA. One of the main design considerations for Dynamo is to give
services control over their system properties, such as durability and
consistency, and to let services make their own tradeoffs between functionality,
performance and cost-effectiveness.

2.3 Design Considerations

Data replication algorithms used in commercial systems traditionally perform
synchronous replica coordination in order to provide a strongly consistent data
access interface. To achieve this level of consistency, these algorithms are
forced to tradeoff the availability of the data under certain failure scenarios.
For instance, rather than dealing with the uncertainty of the correctness of an
answer, the data is made unavailable until it is absolutely certain that it is
correct. From the very early replicated database works, it is well known that
when dealing with the possibility of network failures, strong consistency and
high data availability cannot be achieved simultaneously [2, 11]. As such
systems and applications need to be aware which properties can be achieved under
which conditions.

Riak KV’s Design Considerations

Availability under any circumstances was something we stressed when designing
Riak KV, too. Most databases didn’t enable developers to do this in a simple
way so we set out to change this.

For systems prone to server and network failures, availability can be increased
by using optimistic replication techniques, where changes are allowed to
propagate to replicas in the background, and concurrent, disconnected work is
tolerated. The challenge with this approach is that it can lead to conflicting
changes which must be detected and resolved. This process of conflict resolution
introduces two problems: when to resolve them and who resolves them. Dynamo is
designed to be an eventually consistent data store; that is all updates reach
all replicas eventually.

Remember Eventual Consistency? We followed Dynamo’s lead here and made sure
that Riak KV could withstand network, server and other failures by sacrificing
absolute consistency and building in mechanisms to rectify object conflicts.

An important design consideration is to decide when to perform the process of
resolving update conflicts, i.e., whether conflicts should be resolved during
reads or writes. Many traditional data stores execute conflict resolution during
writes and keep the read complexity simple [7]. In such systems, writes may be
rejected if the data store cannot reach all (or a majority of) the replicas at a
given time. On the other hand, Dynamo targets the design space of an “always
writeable” data store (i.e., a data store that is highly available for writes).
For a number of Amazon services, rejecting customer updates could result in a
poor customer experience. For instance, the shopping cart service must allow
customers to add and remove items from their shopping cart even amidst network
and server failures. This requirement forces us to push the complexity of
conflict resolution to the reads in order to ensure that writes are never
rejected.

Ditto!

The next design choice is who performs the process of conflict resolution. This
can be done by the data store or the application. If conflict resolution is done
by the data store, its choices are rather limited. In such cases, the data store
can only use simple policies, such as “last write wins” [22], to resolve
conflicting updates. On the other hand, since the application is aware of the
data schema it can decide on the conflict resolution method that is best suited
for its client’s experience. For instance, the application that maintains
customer shopping carts can choose to “merge” the conflicting versions and
return a single unified shopping cart. Despite this flexibility, some
application developers may not want to write their own conflict resolution
mechanisms and choose to push it down to the data store, which in turn chooses a
simple policy such as “last write wins”.

Incremental scalability: Dynamo should be able to scale out one storage host
(henceforth, referred to as “node”) at a time, with minimal impact on both
operators of the system and the system itself.

We refer to hosts as “nodes”, too. Riak KV provides a simple set of commands
to start and join nodes to a running cluster. With proper capacity planning,
this process should be painless for the ops team and devs, and imperceivable
by the client.

Symmetry: Every node in Dynamo should have the same set of responsibilities as
its peers; there should be no distinguished node or nodes that take special
roles or extra set of responsibilities. In our experience, symmetry simplifies
the process of system provisioning and maintenance.

Again, we agree. Each storage node is the same at its neighbor. Any node can
coordinate a request and, in the event that a node goes does, its neighbors
can cover for it until it’s restarted or decommissioned.

Decentralization: An extension of symmetry, the design should favor
decentralized peer-to-peer techniques over centralized control. In the past,
centralized control has resulted in outages and the goal is to avoid it as much
as possible. This leads to a simpler, more scalable, and more available system.

A Riak cluster is completely decentralized. No single node is special and this
leads to no single points of failure.

Heterogeneity: The system needs to be able to exploit heterogeneity in the
infrastructure it runs on. e.g. the work distribution must be proportional to
the capabilities of the individual servers. This is essential in adding new
nodes with higher capacity without having to upgrade all hosts at once.

Riak KV agrees.

3. Related Work

This section is not strictly necessary to read for an understanding of how a
Dynamo distributed database functions, especially Riak KV. It’s still an
excellent study of other distributed systems, in some cases ones that helped
inspire Dynamo. When you have time, we highly recommend you read this section.

3.1 Peer to Peer Systems

There are several peer-to-peer (P2P) systems that have looked at the problem of
data storage and distribution. The first generation of P2P systems, such as
Freenet and Gnutella, were predominantly used as file sharing systems. These
were examples of unstructured P2P networks where the overlay links between peers
were established arbitrarily. In these networks, a search query is usually
flooded through the network to find as many peers as possible that share the
data. P2P systems evolved to the next generation into what is widely known as
structured P2P networks. These networks employ a globally consistent protocol to
ensure that any node can efficiently route a search query to some peer that has
the desired data. Systems like Pastry [16] and Chord [20] use routing mechanisms
to ensure that queries can be answered within a bounded number of hops.

To reduce the additional latency introduced by multi-hop routing, some P2P
systems (e.g., [14]) employ O(1) routing where each peer maintains enough
routing information locally so that it can route requests (to access a data
item) to the appropriate peer within a constant number of hops.

Various storage systems, such as Oceanstore [9] and PAST [17] were built on top
of these routing overlays. Oceanstore provides a global, transactional,
persistent storage service that supports serialized updates on widely replicated
data. To allow for concurrent updates while avoiding many of the problems
inherent with wide-area locking, it uses an update model based on conflict
resolution. Conflict resolution was introduced in [21] to reduce the number of
transaction aborts. Oceanstore resolves conflicts by processing a series of
updates, choosing a total order among them, and then applying them atomically in
that order. It is built for an environment where the data is replicated on an
untrusted infrastructure. By comparison, PAST provides a simple abstraction
layer on top of Pastry for persistent and immutable objects. It assumes that the
application can build the necessary storage semantics (such as mutable files) on
top of it.

3.2 Distributed File Systems and Databases

Distributing data for performance, availability and durability has been widely
studied in the file system and database systems community. Compared to P2P
storage systems that only support flat namespaces, distributed file systems
typically support hierarchical namespaces. Systems like Ficus [15] and Coda [19]
replicate files for high availability at the expense of consistency. Update
conflicts are typically managed using specialized conflict resolution
procedures. The Farsite system [1] is a distributed file system that does not
use any centralized server like NFS. Farsite achieves high availability and
scalability using replication. The Google File System [6] is another distributed
file system built for hosting the state of Google’s internal applications. GFS
uses a simple design with a single master server for hosting the entire metadata
and where the data is split into chunks and stored in chunkservers. Bayou is a
distributed relational database system that allows disconnected operations and
provides eventual data consistency [21].

Among these systems, Bayou, Coda and Ficus allow disconnected operations and are
resilient to issues such as network partitions and outages. These systems differ
on their conflict resolution procedures. For instance, Coda and Ficus perform
system level conflict resolution and Bayou allows application level resolution.
All of them, however, guarantee eventual consistency.

Similar to these systems, Dynamo allows read and write operations to continue
even during network partitions and resolves updated conflicts using different
conflict resolution mechanisms. Distributed block storage systems like FAB [18]
split large size objects into smaller blocks and stores each block in a highly
available manner. In comparison to these systems, a key-value store is more
suitable in this case because: (a) it is intended to store relatively small
objects (size < 1M) and (b) key-value stores are easier to configure on a per-
application basis. Antiquity is a wide-area distributed storage system designed
to handle multiple server failures [23]. It uses a secure log to preserve data
integrity, replicates each log on multiple servers for durability, and uses
Byzantine fault tolerance protocols to ensure data consistency. In contrast to
Antiquity, Dynamo does not focus on the problem of data integrity and security
and is built for a trusted environment. Bigtable is a distributed storage system
for managing structured data. It maintains a sparse, multi-dimensional sorted
map and allows applications to access their data using multiple attributes [2].
Compared to Bigtable, Dynamo targets applications that require only key/value
access with primary focus on high availability where updates are not rejected
even in the wake of network partitions or server failures.

This all applies to Riak KV, as well.

Traditional replicated relational database systems focus on the problem of
guaranteeing strong consistency to replicated data. Although strong consistency
provides the application writer a convenient programming model, these systems
are limited in scalability and availability [7]. These systems are not capable
of handling network partitions because they typically provide strong consistency
guarantees.

3.3 Discussion

Dynamo differs from the aforementioned decentralized storage systems in terms of
its target requirements. First, Dynamo is targeted mainly at applications that
need an “always writeable” data store where no updates are rejected due to
failures or concurrent writes. This is a crucial requirement for many Amazon
applications. Second, as noted earlier, Dynamo is built for an infrastructure
within a single administrative domain where all nodes are assumed to be trusted.
Third, applications that use Dynamo do not require support for hierarchical
namespaces (a norm in many file systems) or complex relational schema (supported
by traditional databases). Fourth, Dynamo is built for latency sensitive
applications that require at least 99.9% of read and write operations to be
performed within a few hundred milliseconds. To meet these stringent latency
requirements, it was imperative for us to avoid routing requests through
multiple nodes (which is the typical design adopted by several distributed hash
table systems such as Chord and Pastry). This is because multi-hop routing
increases variability in response times, thereby increasing the latency at
higher percentiles. Dynamo can be characterized as a zero-hop DHT, where each
node maintains enough routing information locally to route a request to the
appropriate node directly.

4.System Architecture

This is truly the meat of the Dynamo paper. Stick around. It gets good.

The architecture of a storage system that needs to operate in a production
setting is complex. In addition to the actual data persistence component, the
system needs to have scalable and robust solutions for load balancing,
membership and failure detection, failure recovery, replica synchronization,
overload handling, state transfer, concurrency and job scheduling, request
marshalling, request routing, system monitoring and alarming, and configuration
management. Describing the details of each of the solutions is not possible, so
this paper focuses on the core distributed systems techniques used in Dynamo:
partitioning, replication, versioning, membership, failure handling and scaling.
Table 1 presents a summary of the list of techniques
Dynamo uses and their respective advantages.

Table 1: Summary of techniques used in Dynamo and their advantages.

Problem

Technique

Advantage

Partitioning

Consistent Hashing

Incremental Scalability

High Availability for writes

Vector clocks with reconciliation during reads

Version size is decoupled from update rates.

Handling temporary failures

Sloppy Quorum and hinted handoff

Provides high availability and durability guarantee when some of the
replicas are not available.

Recovering from permanent failures

Anti-entropy using Merkle trees

Synchronizes divergent replicas in the background.

Membership and failure detection

Gossip-based membership protocol and failure detection.

Preserves symmetry and avoids having a centralized registry for storing
membership and node liveness information.

4.1 System Interface

Dynamo stores objects associated with a key through a simple interface; it
exposes two operations: get() and put(). The get(key) operation locates the
object replicas associated with the key in the storage system and returns a
single object or a list of objects with conflicting versions along with a
context. The put(key, context, object) operation determines where the replicas
of the object should be placed based on the associated key, and writes the
replicas to disk. The context encodes system metadata about the object that is
opaque to the caller and includes information such as the version of the object.
The context information is stored along with the object so that the system can
verify the validity of the context object supplied in the put request.

Whereas Dynamo only has the concept of keys, we added a higher level of
organization called a “bucket.” Keys are stored in buckets and buckets are the
level at which several Riak KV properties can be configured (primarily the “N”
value, or the replication value.) In addition to the bucket+key identifier and
value, Riak KV will also return the associated metadata for a given object
with each get or put.

Dynamo treats both the key and the object supplied by the caller as an opaque
array of bytes. It applies a MD5 hash on the key to generate a 128-bit
identifier, which is used to determine the storage nodes that are responsible
for serving the key.

Riak KV concatenates the bucket with the key and runs it through the SHA1 hash
to generate a 160 bit identifier which is then used to determine where in the
database each datum is stored. Riak KV treats data as an opaque binary, thus
enabling users to store virtually anything.

4.2 Partitioning Algorithm

One of the key design requirements for Dynamo is that it must scale
incrementally. This requires a mechanism to dynamically partition the data over
the set of nodes (i.e., storage hosts) in the system. Dynamo’s partitioning
scheme relies on consistent hashing to distribute the load across multiple
storage hosts. In consistent hashing [10], the output range of a hash function
is treated as a fixed circular space or “ring” (i.e. the largest hash value
wraps around to the smallest hash value). Each node in the system is assigned a
random value within this space which represents its “position” on the ring. Each
data item identified by a key is assigned to a node by hashing the data item’s
key to yield its position on the ring, and then walking the ring clockwise to
find the first node with a position larger than the item’s position. Thus, each
node becomes responsible for the region in the ring between it and its
predecessor node on the ring. The principle advantage of consistent hashing is
that departure or arrival of a node only affects its immediate neighbors and
other nodes remain unaffected.

Partitioning in Riak KV

As mentioned above, Riak KV uses consistent hashing to distribute data around
ring to partitions responsible for storing data. The ring has a maximum key
space of 2^160. Each bucket+key (and its associated value) is hashed to a
location on the ring.

Riak KV also breaks the ring into a set number of partitions. This number is
configured when a cluster is first built. Each node will be responsible for
storing the data hashed to a set number of partitions. Each storage node will
optimistically handle an equal number of partitions.

The basic consistent hashing algorithm presents some challenges. First, the
random position assignment of each node on the ring leads to non-uniform data
and load distribution. Second, the basic algorithm is oblivious to the
heterogeneity in the performance of nodes. To address these issues, Dynamo uses
a variant of consistent hashing (similar to the one used in [10, 20]): instead
of mapping a node to a single point in the circle, each node gets assigned to
multiple points in the ring. To this end, Dynamo uses the concept of “virtual
nodes”. A virtual node looks like a single node in the system, but each node can
be responsible for more than one virtual node. Effectively, when a new node is
added to the system, it is assigned multiple positions (henceforth, “tokens”) in
the ring. The process of fine-tuning Dynamo’s partitioning scheme is discussed
in Section 6.

Riak KV also has the concept of virtual nodes and they are used to the same
end as they are in Dynamo. Physical storage nodes are responsible for
partitions, and each partition a vnode.

Using virtual nodes has the following advantages:

If a node becomes unavailable (due to failures or routine maintenance), the load
handled by this node is evenly dispersed across the remaining available nodes.

When a node becomes available again, or a new node is added to the system, the
newly available node accepts a roughly equivalent amount of load from each of
the other available nodes.

All of these properties for vnodes in Dynamo hold true for Riak KV, too.

The number of virtual nodes that a node is responsible can decided based on its
capacity, accounting for heterogeneity in the physical infrastructure.

4.3 Replication

To achieve high availability and durability, Dynamo replicates its data on
multiple hosts. Each data item is replicated at N hosts, where N is a parameter
configured “per-instance”. Each key, k, is assigned to a coordinator node
(described in the previous section). The coordinator is in charge of the
replication of the data items that fall within its range. In addition to locally
storing each key within its range, the coordinator replicates these keys at the
N-1 clockwise successor nodes in the ring. This results in a system where each
node is responsible for the region of the ring between it and its Nth
predecessor. In Figure 2, node B replicates the key k at
nodes C and D in addition to storing it locally. Node D will store the keys that
fall in the ranges (A, B], (B, C], and (C, D].

Figure 2: Partitioning and replication of keys in Dynamo ring.

Replication in Riak KV, like in Dynamo, is fundamental and automatic. Remember
the concept of a bucket we covered above? In Riak KV, the replication
parameter, “N” (also called “n_val”), is configurable at the bucket level.
The default n_val in Riak KV is 3, meaning that out of the box Riak KV will
store three replicas of your data on three different partitions on the ring.

The diagram is applicable to Riak KV and the manner in which it replicates
data. The preference list is present in Riak KV, too, and is the reason why
any node in the ring can coordinate a request. The node receives a request,
consults the preference list, and routes the request accordingly.

The list of nodes that is responsible for storing a particular key is called the
preference list. The system is designed, as will be explained in Section 4.8, so
that every node in the system can determine which nodes should be in this list
for any particular key. To account for node failures, preference list contains
more than N nodes. Note that with the use of virtual nodes, it is possible that
the first N successor positions for a particular key may be owned by less than N
distinct physical nodes (i.e. a node may hold more than one of the first N
positions). To address this, the preference list for a key is constructed by
skipping positions in the ring to ensure that the list contains only distinct
physical nodes.

4.4 Data Versioning

Dynamo provides eventual consistency, which allows for updates to be propagated
to all replicas asynchronously. A put() call may return to its caller before the
update has been applied at all the replicas, which can result in scenarios where
a subsequent get() operation may return an object that does not have the latest
updates.. If there are no failures then there is a bound on the update
propagation times. However, under certain failure scenarios (e.g., server
outages or network partitions), updates may not arrive at all replicas for an
extended period of time.

Riak KV is an “eventually consistent” database. All replication is done
asynchronously, as you would expect, could result in a datum being returned to
the client that is out of date. But don’t worry. We built in some mechanisms
to address this.

There is a category of applications in Amazon’s platform that can tolerate such
inconsistencies and can be constructed to operate under these conditions. For
example, the shopping cart application requires that an “Add to Cart” operation
can never be forgotten or rejected. If the most recent state of the cart is
unavailable, and a user makes changes to an older version of the cart, that
change is still meaningful and should be preserved. But at the same time it
shouldn’t supersede the currently unavailable state of the cart, which itself
may contain changes that should be preserved. Note that both “add to cart” and
“delete item from cart” operations are translated into put requests to Dynamo.
When a customer wants to add an item to (or remove from) a shopping cart and the
latest version is not available, the item is added to (or removed from) the
older version and the divergent versions are reconciled later.

Much like Dynamo was suited to the design of the shopping cart, Riak KV, and
its tradeoffs, are appropriate for a certain set of use cases. We happen to
feel that most use cases can tolerate some level of eventual consistency.

In order to provide this kind of guarantee, Dynamo treats the result of each
modification as a new and immutable version of the data. It allows for multiple
versions of an object to be present in the system at the same time. Most of the
time, new versions subsume the previous version(s), and the system itself can
determine the authoritative version (syntactic reconciliation). However, version
branching may happen, in the presence of failures combined with concurrent
updates, resulting in conflicting versions of an object. In these cases, the
system cannot reconcile the multiple versions of the same object and the client
must perform the reconciliation in order to collapse multiple branches of data
evolution back into one (semantic reconciliation). A typical example of a
collapse operation is “merging” different versions of a customer’s shopping
cart. Using this reconciliation mechanism, an “add to cart” operation is never
lost. However, deleted items can resurface.

The same holds true for Riak KV. If, by way of some failure and concurrent
update (rare but quite possible), there come to exist multiple versions of the
same object, Riak KV will push this decision down to the client (who are we to
tell you which is the authoritative object?). All that said, if your
application doesn’t need this level of version control, we enable you to turn
the usage of vector clocks on and off at the bucket level.

It is important to understand that certain failure modes can potentially result
in the system having not just two but several versions of the same data. Updates
in the presence of network partitions and node failures can potentially result
in an object having distinct version sub-histories, which the system will need
to reconcile in the future. This requires us to design applications that
explicitly acknowledge the possibility of multiple versions of the same data (in
order to never lose any updates).

Ditto.

Dynamo uses vector clocks [12] in order to capture causality between different
versions of the same object. A vector clock is effectively a list of (node,
counter) pairs. One vector clock is associated with every version of every
object. One can determine whether two versions of an object are on parallel
branches or have a causal ordering, by examine their vector clocks. If the
counters on the first object’s clock are less-than-or-equal to all of the nodes
in the second clock, then the first is an ancestor of the second and can be
forgotten. Otherwise, the two changes are considered to be in conflict and
require reconciliation.

As you may have already figured out, Riak KV uses vector clocks for object
versioning, too. Here are a whole host of resources to keep you busy for a while:

In Dynamo, when a client wishes to update an object, it must specify which
version it is updating. This is done by passing the context it obtained from an
earlier read operation, which contains the vector clock information. Upon
processing a read request, if Dynamo has access to multiple branches that cannot
be syntactically reconciled, it will return all the objects at the leaves, with
the corresponding version information in the context. An update using this
context is considered to have reconciled the divergent versions and the branches
are collapsed into a single new version.

Figure 3: Version evolution of an object over time.

To illustrate the use of vector clocks, let us consider the example shown in
Figure 3. A client writes a new object. The node (say
Sx) that handles the write for this key increases its sequence number and uses
it to create the data’s vector clock. The system now has the object D1 and its
associated clock [(Sx, 1)]. The client updates the object. Assume the same node
handles this request as well. The system now also has object D2 and its
associated clock [(Sx, 2)]. D2 descends from D1 and therefore over-writes D1,
however there may be replicas of D1 lingering at nodes that have not yet seen
D2. Let us assume that the same client updates the object again and a different
server (say Sy) handles the request. The system now has data D3 and its
associated clock [(Sx, 2), (Sy, 1)].

Next assume a different client reads D2 and then tries to update it, and another
node (say Sz) does the write. The system now has D4 (descendant of D2) whose
version clock is [(Sx, 2), (Sz, 1)]. A node that is aware of D1 or D2 could
determine, upon receiving D4 and its clock, that D1 and D2 are overwritten by
the new data and can be garbage collected. A node that is aware of D3 and
receives D4 will find that there is no causal relation between them. In other
words, there are changes in D3 and D4 that are not reflected in each other. Both
versions of the data must be kept and presented to a client (upon a read) for
semantic reconciliation.

Now assume some client reads both D3 and D4 (the context will reflect that both
values were found by the read). The read’s context is a summary of the clocks of
D3 and D4, namely [(Sx, 2), (Sy, 1), (Sz, 1)]. If the client performs the
reconciliation and node Sx coordinates the write, Sx will update its sequence
number in the clock. The new data D5 will have the following clock: [(Sx, 3),
(Sy, 1), (Sz, 1)].

A possible issue with vector clocks is that the size of vector clocks may grow
if many servers coordinate the writes to an object. In practice, this is not
likely because the writes are usually handled by one of the top N nodes in the
preference list. In case of network partitions or multiple server failures,
write requests may be handled by nodes that are not in the top N nodes in the
preference list causing the size of vector clock to grow. In these scenarios, it
is desirable to limit the size of vector clock. To this end, Dynamo employs the
following clock truncation scheme: Along with each (node, counter) pair, Dynamo
stores a timestamp that indicates the last time the node updated the data item.
When the number of (node, counter) pairs in the vector clock reaches a threshold
(say 10), the oldest pair is removed from the clock. Clearly, this truncation
scheme can lead to inefficiencies in reconciliation as the descendant
relationships cannot be derived accurately. However, this problem has not
surfaced in production and therefore this issue has not been thoroughly
investigated.

Riak KV does a certain amount of vector clock pruning to ensure their growth
is kept under control.

4.5 Execution of get () and put () operations

Any storage node in Dynamo is eligible to receive client get and put operations
for any key. In this section, for sake of simplicity, we describe how these
operations are performed in a failure-free environment and in the subsequent
section we describe how read and write operations are executed during failures.

Any node in the Riak KV ring can coordinate a request. The Riak KV information
in this section applies to a failure-free environment.

Both get and put operations are invoked using Amazon’s infrastructure-specific
request processing framework over HTTP. There are two strategies that a client
can use to select a node: (1) route its request through a generic load balancer
that will select a node based on load information, or (2) use a partition-aware
client library that routes requests directly to the appropriate coordinator
nodes. The advantage of the first approach is that the client does not have to
link any code specific to Dynamo in its application, whereas the second strategy
can achieve lower latency because it skips a potential forwarding step.

A node handling a read or write operation is known as the coordinator.
Typically, this is the first among the top N nodes in the preference list. If
the requests are received through a load balancer, requests to access a key may
be routed to any random node in the ring. In this scenario, the node that
receives the request will not coordinate it if the node is not in the top N of
the requested key’s preference list. Instead, that node will forward the request
to the first among the top N nodes in the preference list.

Read and write operations involve the first N healthy nodes in the preference
list, skipping over those that are down or inaccessible. When all nodes are
healthy, the top N nodes in a key’s preference list are accessed. When there are
node failures or network partitions, nodes that are lower ranked in the
preference list are accessed.

To maintain consistency among its replicas, Dynamo uses a consistency protocol
similar to those used in quorum systems. This protocol has two key configurable
values: R and W. R is the minimum number of nodes that must participate in a
successful read operation. W is the minimum number of nodes that must
participate in a successful write operation. Setting R and W such that R + W > N
yields a quorum-like system. In this model, the latency of a get (or put)
operation is dictated by the slowest of the R (or W) replicas. For this reason,
R and W are usually configured to be less than N, to provide better latency.

Riak KV makes use of the same values. But, thanks to our concept of buckets,
we made it a bit more customizable. The default R and W values are set at the
bucket level but can be configured at the request level if the developer deems
it necessary for certain data. “Quorum” as described in Dynamo is the default
setting in Riak KV.

Upon receiving a put() request for a key, the coordinator generates the vector
clock for the new version and writes the new version locally. The coordinator
then sends the new version (along with the new vector clock) to the N highest-
ranked reachable nodes. If at least W-1 nodes respond then the write is
considered successful.

In Riak KV a write is considered successful when the total number of
responding writes equals W. This need not be a durable write, which is a
separate value in Riak KV labeled DW.

Similarly, for a get() request, the coordinator requests all existing versions
of data for that key from the N highest-ranked reachable nodes in the preference
list for that key, and then waits for R responses before returning the result to
the client. If the coordinator ends up gathering multiple versions of the data,
it returns all the versions it deems to be causally unrelated. The divergent
versions are then reconciled and the reconciled version superseding the current
versions is written back.

Same for Riak KV. Reconciling divergent versions in Riak KV is called
Read Repair.

4.6 Handling Failures: Hinted Handoff

If Dynamo used a traditional quorum approach it would be unavailable during
server failures and network partitions, and would have reduced durability even
under the simplest of failure conditions. To remedy this it does not enforce
strict quorum membership and instead it uses a “sloppy quorum”; all read and
write operations are performed on the first N healthy nodes from the preference
list, which may not always be the first N nodes encountered while walking the
consistent hashing ring.

You can glimpse at Riak KV’s preference list (or preflist) calculation in
the Replication walkthrough.

Consider the example of Dynamo configuration given in Figure
2 with N=3. In this example, if node A is temporarily down or unreachable
during a write operation then a replica that would normally have lived on A will
now be sent to node D. This is done to maintain the desired availability and
durability guarantees. The replica sent to D will have a hint in its metadata
that suggests which node was the intended recipient of the replica (in this case
A). Nodes that receive hinted replicas will keep them in a separate local
database that is scanned periodically. Upon detecting that A has recovered, D
will attempt to deliver the replica to A. Once the transfer succeeds, D may
delete the object from its local store without decreasing the total number of
replicas in the system.

Using hinted handoff, Dynamo ensures that the read and write operations are not
failed due to temporary node or network failures. Applications that need the
highest level of availability can set W to 1, which ensures that a write is
accepted as long as a single node in the system has durably written the key it
to its local store. Thus, the write request is only rejected if all nodes in the
system are unavailable. However, in practice, most Amazon services in production
set a higher W to meet the desired level of durability. A more detailed
discussion of configuring N, R and W follows in section 6.

As mentioned previously, Riak KV does not require that a write be durable,
only that a vnode responds in the affirmative. If you require a durable write
in the way mentioned here, use DW.

It is imperative that a highly available storage system be capable of handling
the failure of an entire data center(s). Data center failures happen due to
power outages, cooling failures, network failures, and natural disasters. Dynamo
is configured such that each object is replicated across multiple data centers.
In essence, the preference list of a key is constructed such that the storage
nodes are spread across multiple data centers. These datacenters are connected
through high speed network links. This scheme of replicating across multiple
datacenters allows us to handle entire data center failures without a data
outage.

4.7 Handling permanent failures: Replica synchronization

Hinted handoff works best if the system membership churn is low and node
failures are transient. There are scenarios under which hinted replicas become
unavailable before they can be returned to the original replica node. To handle
this and other threats to durability, Dynamo implements an anti-entropy (replica
synchronization) protocol to keep the replicas synchronized.

Read repair, mentioned above, is the simplest form of anti-entropy. But it is
passive, not active as this section describes.

To detect the inconsistencies between replicas faster and to minimize the amount
of transferred data, Dynamo uses Merkle trees [13]. A Merkle tree is a hash tree
where leaves are hashes of the values of individual keys. Parent nodes higher in
the tree are hashes of their respective children. The principal advantage of
Merkle tree is that each branch of the tree can be checked independently without
requiring nodes to download the entire tree or the entire data set. Moreover,
Merkle trees help in reducing the amount of data that needs to be transferred
while checking for inconsistencies among replicas. For instance, if the hash
values of the root of two trees are equal, then the values of the leaf nodes in
the tree are equal and the nodes require no synchronization. If not, it implies
that the values of some replicas are different. In such cases, the nodes may
exchange the hash values of children and the process continues until it reaches
the leaves of the trees, at which point the hosts can identify the keys that are
“out of sync”. Merkle trees minimize the amount of data that needs to be
transferred for synchronization and reduce the number of disk reads performed
during the anti-entropy process.

Riak KV implements a Merkel-Tree based Active Anti-Entropy (AAE).

Dynamo uses Merkle trees for anti-entropy as follows: Each node maintains a
separate Merkle tree for each key range (the set of keys covered by a virtual
node) it hosts. This allows nodes to compare whether the keys within a key range
are up-to-date. In this scheme, two nodes exchange the root of the Merkle tree
corresponding to the key ranges that they host in common. Subsequently, using
the tree traversal scheme described above the nodes determine if they have any
differences and perform the appropriate synchronization action. The disadvantage
with this scheme is that many key ranges change when a node joins or leaves the
system thereby requiring the tree(s) to be recalculated. This issue is
addressed, however, by the refined partitioning scheme described in Section 6.2.

4.8 Membership and Failure Detection

4.8.1 Ring Membership

In Amazon’s environment node outages (due to failures and maintenance tasks) are
often transient but may last for extended intervals. A node outage rarely
signifies a permanent departure and therefore should not result in rebalancing
of the partition assignment or repair of the unreachable replicas. Similarly,
manual error could result in the unintentional startup of new Dynamo nodes. For
these reasons, it was deemed appropriate to use an explicit mechanism to
initiate the addition and removal of nodes from a Dynamo ring. An administrator
uses a command line tool or a browser to connect to a Dynamo node and issue a
membership change to join a node to a ring or remove a node from a ring. The
node that serves the request writes the membership change and its time of issue
to persistent store. The membership changes form a history because nodes can be
removed and added back multiple times.

Nodes are manually added using the riak-admin cluster join.

When a node permanently departs, rebalancing is triggered using the
riak-admin cluster leave command.

A gossip-based protocol propagates membership changes and maintains an
eventually consistent view of membership. Each node contacts a peer chosen at
random every second and the two nodes efficiently reconcile their persisted
membership change histories.

Riak KV’s ring state holds membership information, and is propgated via
gossiping, including random reconciliation, defaulting to once a minute.

When a node starts for the first time, it chooses its set of tokens (virtual
nodes in the consistent hash space) and maps nodes to their respective token
sets. The mapping is persisted on disk and initially contains only the local
node and token set. The mappings stored at different Dynamo nodes are reconciled
during the same communication exchange that reconciles the membership change
histories. Therefore, partitioning and placement information also propagates via
the gossip-based protocol and each storage node is aware of the token ranges
handled by its peers. This allows each node to forward a key’s read/write
operations to the right set of nodes directly.

These tokens are vnodes (virtual nodes) in Riak KV.

4.8.2 External Discovery

The mechanism described above could temporarily result in a logically
partitioned Dynamo ring. For example, the administrator could contact node A to
join A to the ring, then contact node B to join B to the ring. In this scenario,
nodes A and B would each consider itself a member of the ring, yet neither would
be immediately aware of the other. To prevent logical partitions, some Dynamo
nodes play the role of seeds. Seeds are nodes that are discovered via an
external mechanism and are known to all nodes. Because all nodes eventually
reconcile their membership with a seed, logical partitions are highly unlikely.
Seeds can be obtained either from static configuration or from a configuration
service. Typically seeds are fully functional nodes in the Dynamo ring.

To rectify these sorts of logical partitions, multiple Riak cluster changes
are configured as one batch. Any changes must first be viewed riak-admin
cluster plan, then the changes are committed with riak-admin cluster
commit. The new ring state is gossiped.

4.8.3 Failure Detection

Failure detection in Dynamo is used to avoid attempts to communicate with
unreachable peers during get() and put() operations and when transferring
partitions and hinted replicas. For the purpose of avoiding failed attempts at
communication, a purely local notion of failure detection is entirely
sufficient: node A may consider node B failed if node B does not respond to node
A’s messages (even if B is responsive to node C*s messages). In the presence of
a steady rate of client requests generating inter-node communication in the
Dynamo ring, a node A quickly discovers that a node B is unresponsive when B
fails to respond to a message; Node A then uses alternate nodes to service
requests that map to B’s partitions; A periodically retries B to check for the
latter’s recovery. In the absence of client requests to drive traffic between
two nodes, neither node really needs to know whether the other is reachable and
responsive.

Decentralized failure detection protocols use a simple gossip-style protocol
that enable each node in the system to learn about the arrival (or departure) of
other nodes. For detailed information on decentralized failure detectors and the
parameters affecting their accuracy, the interested reader is referred to [8].
Early designs of Dynamo used a decentralized failure detector to maintain a
globally consistent view of failure state. Later it was determined that the
explicit node join and leave methods obviates the need for a global view of
failure state. This is because nodes are notified of permanent node additions
and removals by the explicit node join and leave methods and temporary node
failures are detected by the individual nodes when they fail to communicate with
others (while forwarding requests).

Riak KV follows the same mechanism, by manually triggering permanent ring
state changes, and gossiping the new state.

4.9 Adding/Removing Storage Nodes

When a new node (say X) is added into the system, it gets assigned a number of
tokens that are randomly scattered on the ring. For every key range that is
assigned to node X, there may be a number of nodes (less than or equal to N)
that are currently in charge of handling keys that fall within its token range.
Due to the allocation of key ranges to X, some existing nodes no longer have to
some of their keys and these nodes transfer those keys to X. Let us consider a
simple bootstrapping scenario where node X is added to the ring shown in
Figure 2 between A and B. When X is added to the system,
it is in charge of storing keys in the ranges (F, G], (G, A] and (A, X]. As a
consequence, nodes B, C and D no longer have to store the keys in these
respective ranges. Therefore, nodes B, C, and D will offer to and upon
confirmation from X transfer the appropriate set of keys. When a node is removed
from the system, the reallocation of keys happens in a reverse process.

Riak KV does not randomly assign vnodes, but rather, iterates through the list
of partitions, assigning them to nodes in a round-robin style.

Operational experience has shown that this approach distributes the load of key
distribution uniformly across the storage nodes, which is important to meet the
latency requirements and to ensure fast bootstrapping. Finally, by adding a
confirmation round between the source and the destination, it is made sure that
the destination node does not receive any duplicate transfers for a given key
range.

5.Implementation

In Dynamo, each storage node has three main software components: request
coordination, membership and failure detection, and a local persistence engine.
All these components are implemented in Java.

Riak KV is implemented in Erlang. Request coordination and membership behavior
is defined by riak_core and implemented by Riak KV.

Dynamo’s local persistence component allows for different storage engines to be
plugged in. Engines that are in use are Berkeley Database (BDB) Transactional
Data Store, BDB Java Edition, MySQL, and an in-memory buffer with persistent
backing store. The main reason for designing a pluggable persistence component
is to choose the storage engine best suited for an application’s access
patterns. For instance, BDB can handle objects typically in the order of tens of
kilobytes whereas MySQL can handle objects of larger sizes. Applications choose
Dynamo’s local persistence engine based on their object size distribution. The
majority of Dynamo’s production instances use BDB Transactional Data Store.

Riak KV ships with various backend options. Bitcask is the default, but
LevelDB and Main Memory are also used heavily in production (in that
order). You can also use more than one backend in production via the [[Multi]]
backend configuration.

Bitcask is a fast and reliable choice, but does have some limitations at very
large scales. For larger clusters, you may want to choose LevelDB (which also
supports secondary indexes). The Memory backend is an excellent choice when
speed is important and durability is not. It also has TTL support.

The request coordination component is built on top of an event-driven messaging
substrate where the message processing pipeline is split into multiple stages
similar to the SEDA architecture [24]. All communications are implemented using
Java NIO channels. The coordinator executes the read and write requests on
behalf of clients by collecting data from one or more nodes (in the case of
reads) or storing data at one or more nodes (for writes). Each client request
results in the creation of a state machine on the node that received the client
request. The state machine contains all the logic for identifying the nodes
responsible for a key, sending the requests, waiting for responses, potentially
doing retries, processing the replies and packaging the response to the client.
Each state machine instance handles exactly one client request. For instance, a
read operation implements the following state machine: (i) send read requests to
the nodes, (ii) wait for minimum number of required responses, (iii) if too few
replies were received within a given time bound, fail the request, (iv)
otherwise gather all the data versions and determine the ones to be returned and
(v) if versioning is enabled, perform syntactic reconciliation and generate an
opaque write context that contains the vector clock that subsumes all the
remaining versions. For the sake of brevity the failure handling and retry
states are left out.

Request coordination in Riak KV uses Erlang message passing, but follows a
similar state machine.

After the read response has been returned to the caller the state machine waits
for a small period of time to receive any outstanding responses. If stale
versions were returned in any of the responses, the coordinator updates those
nodes with the latest version. This process is called read repair because it
repairs replicas that have missed a recent update at an opportunistic time and
relieves the anti-entropy protocol from having to do it.

As noted earlier, write requests are coordinated by one of the top N nodes in
the preference list. Although it is desirable always to have the first node
among the top N to coordinate the writes thereby serializing all writes at a
single location, this approach has led to uneven load distribution resulting in
SLA violations. This is because the request load is not uniformly distributed
across objects. To counter this, any of the top N nodes in the preference list
is allowed to coordinate the writes. In particular, since each write usually
follows a read operation, the coordinator for a write is chosen to be the node
that replied fastest to the previous read operation which is stored in the
context information of the request. This optimization enables us to pick the
node that has the data that was read by the preceding read operation thereby
increasing the chances of getting “read-your-writes” consistency. It also
reduces variability in the performance of the request handling which improves
the performance at the 99.9 percentile.

6. Experiences & Lessons Learned

Much of this section relates to benchmarks run against Dynamo. You can run
Basho Bench against your own Riak cluster to discover your own
optimal values.

Dynamo is used by several services with different configurations. These
instances differ by their version reconciliation logic, and read/write quorum
characteristics. The following are the main patterns in which Dynamo is used:

Business logic specific reconciliation: This is a popular use case for Dynamo.
Each data object is replicated across multiple nodes. In case of divergent
versions, the client application performs its own reconciliation logic. The
shopping cart service discussed earlier is a prime example of this category. Its
business logic reconciles objects by merging different versions of a customer’s
shopping cart.

Riak KV currently supports simple conflict resolution by way of read-repair,
remanding more complex reconciliation to the client. There are several tools
to help simplify this task, such as Statebox.

Riak KV supports a simple reconciliation strategy, called CRDTs, for reconciling common data types like sets and
counters.

Timestamp based reconciliation: This case differs from the previous one only
in the reconciliation mechanism. In case of divergent versions, Dynamo performs
simple timestamp based reconciliation logic of “last write wins”; i.e., the
object with the largest physical timestamp value is chosen as the correct
version. The service that maintains customer’s session information is a good
example of a service that uses this mode.

Riak also supports this for high-performance cases where accuracy is less
important than speed.

High performance read engine: While Dynamo is built to be an “always
writeable” data store, a few services are tuning its quorum characteristics and
using it as a high performance read engine. Typically, these services have a
high read request rate and only a small number of updates. In this
configuration, typically R is set to be 1 and W to be N. For these services,
Dynamo provides the ability to partition and replicate their data across
multiple nodes thereby offering incremental scalability. Some of these instances
function as the authoritative persistence cache for data stored in more heavy
weight backing stores. Services that maintain product catalog and promotional
items fit in this category.

Riak can be used in this manner.

The main advantage of Dynamo is that its client applications can tune the values
of N, R and W to achieve their desired levels of performance, availability and
durability. For instance, the value of N determines the durability of each
object. A typical value of N used by Dynamo’s users is 3.

The values of W and R impact object availability, durability and consistency.
For instance, if W is set to 1, then the system will never reject a write
request as long as there is at least one node in the system that can
successfully process a write request. However, low values of W and R can
increase the risk of inconsistency as write requests are deemed successful and
returned to the clients even if they are not processed by a majority of the
replicas. This also introduces a vulnerability window for durability when a
write request is successfully returned to the client even though it has been
persisted at only a small number of nodes.

Traditional wisdom holds that durability and availability go hand-in-hand.
However, this is not necessarily true here. For instance, the vulnerability
window for durability can be decreased by increasing W. This may increase the
probability of rejecting requests (thereby decreasing availability) because more
storage hosts need to be alive to process a write request.

The common (N,R,W) configuration used by several instances of Dynamo is (3,2,2).
These values are chosen to meet the necessary levels of performance, durability,
consistency, and availability SLAs.

All the measurements presented in this section were taken on a live system
operating with a configuration of (3,2,2) and running a couple hundred nodes
with homogenous hardware configurations. As mentioned earlier, each instance of
Dynamo contains nodes that are located in multiple datacenters. These
datacenters are typically connected through high speed network links. Recall
that to generate a successful get (or put) response R (or W) nodes need to
respond to the coordinator. Clearly, the network latencies between datacenters
affect the response time and the nodes (and their datacenter locations) are
chosen such that the applications target SLAs are met.

Ditto for Riak.

6.1 Balancing Performance and Durability

While Dynamo’s principle design goal is to build a highly available data store,
performance is an equally important criterion in Amazon’s platform. As noted
earlier, to provide a consistent customer experience, Amazon’s services set
their performance targets at higher percentiles (such as the 99.9th or 99.99th
percentiles). A typical SLA required of services that use Dynamo is that 99.9%
of the read and write requests execute within 300ms.

Since Dynamo is run on standard commodity hardware components that have far less
I/O throughput than high-end enterprise servers, providing consistently high
performance for read and write operations is a non-trivial task. The involvement
of multiple storage nodes in read and write operations makes it even more
challenging, since the performance of these operations is limited by the slowest
of the R or W replicas. Figure 4 shows the average and
99.9th percentile latencies of Dynamo’s read and write operations during a
period of 30 days. As seen in the figure, the latencies exhibit a clear diurnal
pattern which is a result of the diurnal pattern in the incoming request rate
(i.e., there is a significant difference in request rate between the daytime and
night). Moreover, the write latencies are higher than read latencies obviously
because write operations always results in disk access. Also, the 99.9th
percentile latencies are around 200 ms and are an order of magnitude higher than
the averages. This is because the 99.9th percentile latencies are affected by
several factors such as variability in request load, object sizes, and locality
patterns.

Figure 4: Average and 99.9 percentiles of latencies for read and write
requests during our peak request season of December 2006. The intervals
between consecutive ticks in the x-axis correspond to 12 hours. Latencies
follow a diurnal pattern similar to the request rate and 99.9 percentile
latencies are an order of magnitude higher than averages.

While this level of performance is acceptable for a number of services, a few
customer-facing services required higher levels of performance. For these
services, Dynamo provides the ability to trade-off durability guarantees for
performance. In the optimization each storage node maintains an object buffer in
its main memory. Each write operation is stored in the buffer and gets
periodically written to storage by a writer thread. In this scheme, read
operations first check if the requested key is present in the buffer. If so, the
object is read from the buffer instead of the storage engine.

This is more similar to Riak’s W value, since only DW requires a durable write
to respond as a success.

This optimization has resulted in lowering the 99.9th percentile latency by a
factor of 5 during peak traffic even for a very small buffer of a thousand
objects (see Figure 5). Also, as seen in the figure,
write buffering smoothes out higher percentile latencies. Obviously, this scheme
trades durability for performance. In this scheme, a server crash can result in
missing writes that were queued up in the buffer. To reduce the durability risk,
the write operation is refined to have the coordinator choose one out of the N
replicas to perform a “durable write”. Since the coordinator waits only for W
responses, the performance of the write operation is not affected by the
performance of the durable write operation performed by a single replica.

Figure 5: Comparison of performance of 99.9th percentile latencies for
buffered vs. non-buffered writes over a period of 24 hours. The intervals
between consecutive ticks in the x-axis correspond to one hour.

Setting DW=1 will replicate this behavior.

6.2 Ensuring Uniform Load distribution

Dynamo uses consistent hashing to partition its key space across its replicas
and to ensure uniform load distribution. A uniform key distribution can help us
achieve uniform load distribution assuming the access distribution of keys is
not highly skewed. In particular, Dynamo’s design assumes that even where there
is a significant skew in the access distribution there are enough keys in the
popular end of the distribution so that the load of handling popular keys can be
spread across the nodes uniformly through partitioning. This section discusses
the load imbalance seen in Dynamo and the impact of different partitioning
strategies on load distribution.

To study the load imbalance and its correlation with request load, the total
number of requests received by each node was measured for a period of 24 hours -
broken down into intervals of 30 minutes. In a given time window, a node is
considered to be “in-balance”, if the node’s request load deviates from the
average load by a value a less than a certain threshold (here 15%). Otherwise
the node was deemed “out-of-balance”. Figure 6 presents
the fraction of nodes that are “out-of-balance” (henceforth, “imbalance ratio”)
during this time period. For reference, the corresponding request load received
by the entire system during this time period is also plotted. As seen in the
figure, the imbalance ratio decreases with increasing load. For instance, during
low loads the imbalance ratio is as high as 20% and during high loads it is
close to 10%. Intuitively, this can be explained by the fact that under high
loads, a large number of popular keys are accessed and due to uniform
distribution of keys the load is evenly distributed. However, during low loads
(where load is 1/8th of the measured peak load), fewer popular keys are
accessed, resulting in a higher load imbalance.

Figure 6: Fraction of nodes that are out-of-balance (i.e., nodes whose
request load is above a certain threshold from the average system load) and
their corresponding request load. The interval between ticks in x-axis
corresponds to a time period of 30 minutes.

This section discusses how Dynamo’s partitioning scheme has evolved over time
and its implications on load distribution.

Strategy 1: T random tokens per node and partition by token
value: This was the initial strategy deployed in production (and described in
Section 4.2). In this scheme, each node is assigned T tokens (chosen uniformly
at random from the hash space). The tokens of all nodes are ordered according to
their values in the hash space. Every two consecutive tokens define a range. The
last token and the first token form a range that “wraps” around from the highest
value to the lowest value in the hash space. Because the tokens are chosen
randomly, the ranges vary in size. As nodes join and leave the system, the token
set changes and consequently the ranges change. Note that the space needed to
maintain the membership at each node increases linearly with the number of nodes
in the system.

Riak uses equal sized partitions with a round-robin distribution–not a
variably-sized partitions that are randomly distributed.

While using this strategy, the following problems were encountered. First, when
a new node joins the system, it needs to “steal” its key ranges from other
nodes. However, the nodes handing the key ranges off to the new node have to
scan their local persistence store to retrieve the appropriate set of data
items. Note that performing such a scan operation on a production node is tricky
as scans are highly resource intensive operations and they need to be executed
in the background without affecting the customer performance. This requires us
to run the bootstrapping task at the lowest priority. However, this
significantly slows the bootstrapping process and during busy shopping season,
when the nodes are handling millions of requests a day, the bootstrapping has
taken almost a day to complete. Second, when a node joins/leaves the system, the
key ranges handled by many nodes change and the Merkle trees for the new ranges
need to be recalculated, which is a non-trivial operation to perform on a
production system. Finally, there was no easy way to take a snapshot of the
entire key space due to the randomness in key ranges, and this made the process
of archival complicated. In this scheme, archiving the entire key space requires
us to retrieve the keys from each node separately, which is highly inefficient.

The fundamental issue with this strategy is that the schemes for data
partitioning and data placement are intertwined. For instance, in some cases, it
is preferred to add more nodes to the system in order to handle an increase in
request load. However, in this scenario, it is not possible to add nodes without
affecting data partitioning. Ideally, it is desirable to use independent schemes
for partitioning and placement. To this end, following strategies were
evaluated:

Strategy 2: T random tokens per node and equal sized
partitions: In this strategy, the hash space is divided into Q equally sized
partitions/ranges and each node is assigned T random tokens. Q is usually set
such that Q >> N and Q >> S*T, where S is the number of nodes in the system. In
this strategy, the tokens are only used to build the function that maps values
in the hash space to the ordered lists of nodes and not to decide the
partitioning. A partition is placed on the first N unique nodes that are
encountered while walking the consistent hashing ring clockwise from the end of
the partition. Figure 7 illustrates this strategy for
N=3. In this example, nodes A, B, C are encountered while walking the ring from
the end of the partition that contains key k1. The primary advantages of this
strategy are: (i) decoupling of partitioning and partition placement, and (ii)
enabling the possibility of changing the placement scheme at runtime.

As before mentioned, Riak uses equal sized partitions, but not
random distribution.

Figure 7: Partitioning and placement of keys in the three strategies. A, B,
and C depict the three unique nodes that form the preference list for the
key k1 on the consistent hashing ring (N=3). The shaded area indicates the
key range for which nodes A, B, and C form the preference list. Dark arrows
indicate the token locations for various nodes.

Strategy 3: Q/S tokens per node, equal-sized partitions:
Similar to strategy 2, this strategy divides the hash space into Q equally sized
partitions and the placement of partition is decoupled from the partitioning
scheme. Moreover, each node is assigned Q/S tokens where S is the number of
nodes in the system. When a node leaves the system, its tokens are randomly
distributed to the remaining nodes such that these properties are preserved.
Similarly, when a node joins the system it “steals” tokens from nodes in the
system in a way that preserves these properties.

The efficiency of these three strategies is evaluated for a system with S=30 and
N=3. However, comparing these different strategies in a fair manner is hard as
different strategies have different configurations to tune their efficiency. For
instance, the load distribution property of strategy 1 depends on the number of
tokens (i.e., T) while strategy 3 depends on the number of partitions (i.e., Q).
One fair way to compare these strategies is to evaluate the skew in their load
distribution while all strategies use the same amount of space to maintain their
membership information. For instance, in strategy 1 each node needs to maintain
the token positions of all the nodes in the ring and in strategy 3 each node
needs to maintain the information regarding the partitions assigned to each
node.

In our next experiment, these strategies were evaluated by varying the relevant
parameters (T and Q). The load balancing efficiency of each strategy was
measured for different sizes of membership information that needs to be
maintained at each node, where Load balancing efficiency is defined as the ratio
of average number of requests served by each node to the maximum number of
requests served by the hottest node.

The results are given in Figure 8. As seen in the
figure, strategy 3 achieves the best load balancing efficiency and strategy 2
has the worst load balancing efficiency. For a brief time, Strategy 2 served as
an interim setup during the process of migrating Dynamo instances from using
Strategy 1 to Strategy 3. Compared to Strategy 1, Strategy 3 achieves better
efficiency and reduces the size of membership information maintained at each
node by three orders of magnitude. While storage is not a major issue the nodes
gossip the membership information periodically and as such it is desirable to
keep this information as compact as possible. In addition to this, strategy 3 is
advantageous and simpler to deploy for the following reasons: (i) Faster
bootstrapping/recovery: Since partition ranges are fixed, they can be stored in
separate files, meaning a partition can be relocated as a unit by simply
transferring the file (avoiding random accesses needed to locate specific
items). This simplifies the process of bootstrapping and recovery. (ii) Ease of
archival: Periodical archiving of the dataset is a mandatory requirement for
most of Amazon storage services. Archiving the entire dataset stored by Dynamo
is simpler in strategy 3 because the partition files can be archived separately.
By contrast, in Strategy 1, the tokens are chosen randomly and, archiving the
data stored in Dynamo requires retrieving the keys from individual nodes
separately and is usually inefficient and slow. The disadvantage of strategy 3
is that changing the node membership requires coordination in order to preserve
the properties required of the assignment.

Figure 8: Comparison of the load distribution efficiency of different
strategies for system with 30 nodes and N=3 with equal amount of metadata
maintained at each node. The values of the system size and number of
replicas are based on the typical configuration deployed for majority of
our services.

6.3 Divergent Versions: When and How Many?

As noted earlier, Dynamo is designed to tradeoff consistency for availability.
To understand the precise impact of different failures on consistency, detailed
data is required on multiple factors: outage length, type of failure, component
reliability, workload etc. Presenting these numbers in detail is outside of the
scope of this paper. However, this section discusses a good summary metric: the
number of divergent versions seen by the application in a live production
environment.

This first statement should be read carefully. It’s probably more correct to
say that Dynamo (and Riak) provides no consistency guarantees, and allows
users to trade availability for durability/latency.

Divergent versions of a data item arise in two scenarios. The first is when the
system is facing failure scenarios such as node failures, data center failures,
and network partitions. The second is when the system is handling a large number
of concurrent writers to a single data item and multiple nodes end up
coordinating the updates concurrently. From both a usability and efficiency
perspective, it is preferred to keep the number of divergent versions at any
given time as low as possible. If the versions cannot be syntactically
reconciled based on vector clocks alone, they have to be passed to the business
logic for semantic reconciliation. Semantic reconciliation introduces additional
load on services, so it is desirable to minimize the need for it.

In our next experiment, the number of versions returned to the shopping cart
service was profiled for a period of 24 hours. During this period, 99.94% of
requests saw exactly one version; 0.00057% of requests saw 2 versions; 0.00047%
of requests saw 3 versions and 0.00009% of requests saw 4 versions. This shows
that divergent versions are created rarely.

Experience shows that the increase in the number of divergent versions is
contributed not by failures but due to the increase in number of concurrent
writers. The increase in the number of concurrent writes is usually triggered by
busy robots (automated client programs) and rarely by humans. This issue is not
discussed in detail due to the sensitive nature of the story.

6.4 Client-driven or Server-driven Coordination

As mentioned in Section 5, Dynamo has a request coordination component that uses
a state machine to handle incoming requests. Client requests are uniformly
assigned to nodes in the ring by a load balancer. Any Dynamo node can act as a
coordinator for a read request. Write requests on the other hand will be
coordinated by a node in the key’s current preference list. This restriction is
due to the fact that these preferred nodes have the added responsibility of
creating a new version stamp that causally subsumes the version that has been
updated by the write request. Note that if Dynamo’s versioning scheme is based
on physical timestamps, any node can coordinate a write request.

In Riak, a server-side load-balancer is an optional configuration. You
generally use either virtual IPs or reverse-proxies.

An alternative approach to request coordination is to move the state machine to
the client nodes. In this scheme client applications use a library to perform
request coordination locally. A client periodically picks a random Dynamo node
and downloads its current view of Dynamo membership state. Using this
information the client can determine which set of nodes form the preference list
for any given key. Read requests can be coordinated at the client node thereby
avoiding the extra network hop that is incurred if the request were assigned to
a random Dynamo node by the load balancer. Writes will either be forwarded to a
node in the key’s preference list or can be coordinated locally if Dynamo is
using timestamps based versioning.

Note that the Riak clients do not coordinate with Riak’s preference list, but
simply round-robin requests, letting the Riak cluster handle routing.

An important advantage of the client-driven coordination approach is that a load
balancer is no longer required to uniformly distribute client load. Fair load
distribution is implicitly guaranteed by the near uniform assignment of keys to
the storage nodes. Obviously, the efficiency of this scheme is dependent on how
fresh the membership information is at the client. Currently clients poll a
random Dynamo node every 10 seconds for membership updates. A pull based
approach was chosen over a push based one as the former scales better with large
number of clients and requires very little state to be maintained at servers
regarding clients. However, in the worst case the client can be exposed to stale
membership for duration of 10 seconds. In case, if the client detects its
membership table is stale (for instance, when some members are unreachable), it
will immediately refresh its membership information.

Table 2 shows the latency improvements at the 99.9th
percentile and averages that were observed for a period of 24 hours using
client-driven coordination compared to the server-driven approach. As seen in
the table, the client-driven coordination approach reduces the latencies by at
least 30 milliseconds for 99.9th percentile latencies and decreases the average
by 3 to 4 milliseconds. The latency improvement is because the client-driven
approach eliminates the overhead of the load balancer and the extra network hop
that may be incurred when a request is assigned to a random node. As seen in the
table, average latencies tend to be significantly lower than latencies at the
99.9th percentile. This is because Dynamo’s storage engine caches and write
buffer have good hit ratios. Moreover, since the load balancers and network
introduce additional variability to the response time, the gain in response time
is higher for the 99.9th percentile than the average.

6.5 Balancing background vs. foreground tasks

Each node performs different kinds of background tasks for replica
synchronization and data handoff (either due to hinting or adding/removing
nodes) in addition to its normal foreground put/get operations. In early
production settings, these background tasks triggered the problem of resource
contention and affected the performance of the regular put and get operations.
Hence, it became necessary to ensure that background tasks ran only when the
regular critical operations are not affected significantly. To this end, the
background tasks were integrated with an admission control mechanism. Each of
the background tasks uses this controller to reserve runtime slices of the
resource (e.g. database), shared across all background tasks. A feedback
mechanism based on the monitored performance of the foreground tasks is employed
to change the number of slices that are available to the background tasks.

Riak does this, too. For example, hinted handoff runs in the background at a
low level, so as not to overwhelm a cluster when nodes are added/removed.

The admission controller constantly monitors the behavior of resource accesses
while executing a “foreground” put/get operation. Monitored aspects include
latencies for disk operations, failed database accesses due to lock-contention
and transaction timeouts, and request queue wait times. This information is used
to check whether the percentiles of latencies (or failures) in a given trailing
time window are close to a desired threshold. For example, the background
controller checks to see how close the 99th percentile database read latency
(over the last 60 seconds) is to a preset threshold (say 50ms). The controller
uses such comparisons to assess the resource availability for the foreground
operations. Subsequently, it decides on how many time slices will be available
to background tasks, thereby using the feedback loop to limit the intrusiveness
of the background activities. Note that a similar problem of managing background
tasks has been studied in [4].

6.6 Discussion

This section summarizes some of the experiences gained during the process of
implementation and maintenance of Dynamo. Many Amazon internal services have
used Dynamo for the past two years and it has provided significant levels of
availability to its applications. In particular, applications have received
successful responses (without timing out) for 99.9995% of its requests and no
data loss event has occurred to date.

Moreover, the primary advantage of Dynamo is that it provides the necessary
knobs using the three parameters of (N,R,W) to tune their instance based on
their needs.. Unlike popular commercial data stores, Dynamo exposes data
consistency and reconciliation logic issues to the developers. At the outset,
one may expect the application logic to become more complex. However,
historically, Amazon’s platform is built for high availability and many
applications are designed to handle different failure modes and inconsistencies
that may arise. Hence, porting such applications to use Dynamo was a relatively
simple task. For new applications that want to use Dynamo, some analysis is
required during the initial stages of the development to pick the right conflict
resolution mechanisms that meet the business case appropriately. Finally, Dynamo
adopts a full membership model where each node is aware of the data hosted by
its peers. To do this, each node actively gossips the full routing table with
other nodes in the system. This model works well for a system that contains
couple of hundreds of nodes. However, scaling such a design to run with tens of
thousands of nodes is not trivial because the overhead in maintaining the
routing table increases with the system size. This limitation might be overcome
by introducing hierarchical extensions to Dynamo. Also, note that this problem
is actively addressed by O(1) DHT systems(e.g., [14]).

This is equally true for Riak. As mentioned above, consider running
Basho Bench to help discover your optimal setup. Nothing will give you
better numbers than real experimentation.

7. Conclusions

This paper was an overview of Riak from a Dynamo point-of-view. To get a
better sense of the Riak ecosystem, read our ever-expanding documentation.

This paper described Dynamo, a highly available and scalable data store, used
for storing state of a number of core services of Amazon.com’s e-commerce
platform. Dynamo has provided the desired levels of availability and performance
and has been successful in handling server failures, data center failures and
network partitions. Dynamo is incrementally scalable and allows service owners
to scale up and down based on their current request load. Dynamo allows service
owners to customize their storage system to meet their desired performance,
durability and consistency SLAs by allowing them to tune the parameters N, R,
and W.

The production use of Dynamo for the past year demonstrates that decentralized
techniques can be combined to provide a single highly-available system. Its
success in one of the most challenging application environments shows that an
eventual-consistent storage system can be a building block for highly-available
applications.